--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2-7B metrics: - accuracy model-index: - name: QWEN_FACT_updates results: [] --- # QWEN_FACT_updates This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5144 - Balanced Accuracy: 0.7801 - Accuracy: 0.7998 - Micro F1: 0.7998 - Macro F1: 0.7392 - Weighted F1: 0.8114 - Classification Report: precision recall f1-score support 0 0.92 0.81 0.86 857 1 0.52 0.75 0.61 232 accuracy 0.80 1089 macro avg 0.72 0.78 0.74 1089 weighted avg 0.84 0.80 0.81 1089 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Accuracy | Balanced Accuracy | Classification Report | Validation Loss | Macro F1 | Micro F1 | Weighted F1 | |:-------------:|:-----:|:----:|:--------:|:-----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:--------:|:--------:|:-----------:| | 0.6846 | 1.0 | 391 | 0.7980 | 0.7553 | precision recall f1-score support 0 0.91 0.83 0.87 857 1 0.52 0.68 0.59 232 accuracy 0.80 1089 macro avg 0.71 0.76 0.73 1089 weighted avg 0.82 0.80 0.81 1089 | 0.5173 | 0.7278 | 0.7980 | 0.8071 | | 0.5021 | 2.0 | 782 | 0.8044 | 0.7673 | precision recall f1-score support 0 0.91 0.83 0.87 857 1 0.53 0.70 0.60 232 accuracy 0.80 1089 macro avg 0.72 0.77 0.74 1089 weighted avg 0.83 0.80 0.81 1089 | 0.4834 | 0.7374 | 0.8044 | 0.8135 | | 0.408 | 3.0 | 1173 | 0.8356 | 0.7667 | precision recall f1-score support 0 0.90 0.89 0.89 857 1 0.61 0.65 0.63 232 accuracy 0.84 1089 macro avg 0.75 0.77 0.76 1089 weighted avg 0.84 0.84 0.84 1089 | 0.4296 | 0.7605 | 0.8356 | 0.8375 | | 0.3032 | 4.0 | 1564 | 0.7511 | 0.7712 | precision recall f1-score support 0 0.93 0.74 0.82 857 1 0.45 0.81 0.58 232 accuracy 0.75 1089 macro avg 0.69 0.77 0.70 1089 weighted avg 0.83 0.75 0.77 1089 | 0.5927 | 0.7015 | 0.7511 | 0.7714 | | 0.234 | 5.0 | 1955 | 0.5144 | 0.7801 | 0.7998 | 0.7998 | 0.7392 | 0.8114 | precision recall f1-score support 0 0.92 0.81 0.86 857 1 0.52 0.75 0.61 232 accuracy 0.80 1089 macro avg 0.72 0.78 0.74 1089 weighted avg 0.84 0.80 0.81 1089 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1